Báo cáo khoa học: " Monte Carlo dose verification of prostate patients treated with simultaneous integrated boost intensity modulated radiation therapy" ppsx

BioMed Central
Page 1 of 17
(page number not for citation purposes)
Radiation Oncology
Open Access
Research
Monte Carlo dose verification of prostate patients treated with
simultaneous integrated boost intensity modulated radiation
therapy
Nesrin Dogan*
1
, Ivaylo Mihaylov
2
, Yan Wu
1
, Paul J Keall
3
, Jeffrey V Siebers
1

and Michael P Hagan
1
Address:
1
Virginia Commonwealth University Medical Center, Radiation Oncology Department, 401 College Street, Richmond, Virginia 23298,
USA,
2
Department of Radiation Oncology, University of Arkansas for Medical Sciences, 4301 W. Markham Street, Little Rock, Arizona 72205, USA
and
3
Department of Radiation Oncology, Stanford University Cancer Center, 875 Blake Wilbur Drive, Stanford, California 94305, USA

Email: Nesrin Dogan* - ; Ivaylo Mihaylov - ; Yan Wu - ;
Paul J Keall - ; Jeffrey V Siebers - ; Michael P Hagan -
* Corresponding author
Abstract
Background: To evaluate the dosimetric differences between Superposition/Convolution (SC)
and Monte Carlo (MC) calculated dose distributions for simultaneous integrated boost (SIB)
prostate cancer intensity modulated radiotherapy (IMRT) compared to experimental (film)
measurements and the implications for clinical treatments.
Methods: Twenty-two prostate patients treated with an in-house SIB-IMRT protocol were
selected. SC-based plans used for treatment were re-evaluated with EGS4-based MC calculations
for treatment verification. Accuracy was evaluated with-respect-to film-based dosimetry.
Comparisons used gamma (γ)-index, distance-to-agreement (DTA), and superimposed dose
distributions. The treatment plans were also compared based on dose-volume indices and 3-D γ
index for targets and critical structures.
Results: Flat-phantom comparisons demonstrated that the MC algorithm predicted
measurements better than the SC algorithm. The average PTV
prostate
D
98
agreement between SC
and MC was 1.2% ± 1.1. For rectum, the average differences in SC and MC calculated D
50
ranged
from -3.6% to 3.4%. For small bowel, there were up to 30.2% ± 40.7 (range: 0.2%, 115%) differences
between SC and MC calculated average D
50
index. For femurs, the differences in average D
50
reached up to 8.6% ± 3.6 (range: 1.2%, 14.5%). For PTV
prostate

and PTV
nodes
, the average gamma
scores were >95.0%.
Conclusion: MC agrees better with film measurements than SC. Although, on average, SC-
calculated doses agreed with MC calculations within the targets within 2%, there were deviations
up to 5% for some patient's treatment plans. For some patients, the magnitude of such deviations
might decrease the intended target dose levels that are required for the treatment protocol, placing
the patients in different dose levels that do not satisfy the protocol dose requirements.
Published: 15 June 2009
Radiation Oncology 2009, 4:18 doi:10.1186/1748-717X-4-18
Received: 12 February 2009
Accepted: 15 June 2009
This article is available from: />© 2009 Dogan et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( />),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Radiation Oncology 2009, 4:18 />Page 2 of 17
(page number not for citation purposes)
Background
High-dose calculation accuracy and beam delivery is very
important for Intensity Modulated Radiotherapy (IMRT).
IMRT is typically delivered through a sequence of small
fields or with a dynamically moving aperture and sharper
dose gradients near boundaries are very common in IMRT
fields [1-3]. Most IMRT systems utilize simple and fast
dose-calculation algorithms, such as the pencil beam
method, during the optimization process. In many sys-
tems, a more accurate algorithm, such as the Superposi-
tion/Convolution (SC) method, is used for the final dose
calculation after leaf sequencing process. However, even

relatively sophisticated semi-analytical dose-calculation
algorithms such as SC method can be inaccurate for small
fields (>3%), especially in regions of dose gradients, in
regions of tissue heterogeneities, and for the estimation of
multileaf collimator (MLC) leakage [4-6]. Furthermore,
treatment fields for simultaneous integrated boost (SIB)
IMRT techniques often have larger intensity variations
which result in complex MLC patterns and present chal-
lenges to dose calculations algorithms because of the
effects of radiation transmitted through and scattered
from the MLC [7]. For such fields, assumptions used in
conventional dose calculation algorithms may break
down, causing large dose prediction errors[8,9]. In addi-
tion to the dose calculation algorithm type, the major fac-
tors of influencing dose calculation accuracy are the beam
modeling and the user specific commissioning and tuning
of the dose calculation model to match IMRT dose distri-
butions for a particular accelerator.
It has been shown that the use of an algorithm such as
Monte Carlo (MC) that can explicitly account for MLC
leakage and scatter can provide more improved dose cal-
culation accuracy when compared to measurements [10-
12]. Several investigators have now reported on the suc-
cessful implementation of MC in clinical settings [10-25].
As a result, MC dose-calculation algorithms have been
implemented for dosimetric verification of IMRT patient
treatment plans.
One such work done by Yang et al.[14] investigated the
accuracy of the CORVUS finite size pencil beam algorithm
to the MC method for thirty prostate step-and-shoot IMRT

plans utilizing both coplanar and non-coplanar beam
arrangements. Their work, however, did not compare the
MC re-calculated IMRT plans with measurements. MC cal-
culations were preformed using EGS4/PRESTA
code[13,20]. Their work compared the differences
between CORVUS generated and MC recalculated IMRT
plans in terms of differences in isodose distributions and
dose volume histograms (DVHs). Their MC dose calcula-
tions, as compared to the CORVUS pencil beam algo-
rithm, showed that while the differences in minimum
target dose without heterogeneity corrections between
two algorithms were within 4%, the differences in maxi-
mum dose to the bladder and rectum were <4% for
twenty-five coplanar beam plans. For IMRT plans with
non-coplanar beam arrangements without heterogeneity
corrections, their results showed that the differences
between MC and Corvus calculated doses to the CTV were
>3% for all cases. For some cases, >9% differences in the
minimum target dose was observed. The authors elabo-
rated that this was probably due to the excessive attenua-
tion of non-coplanar beams through the femurs. When
the CORVUS heterogeneity corrections were turned on,
the differences in mean target dose between MC and
CORVUS were reduced to ~4%. The authors suggested
that the IMRT plans utilizing non-coplanar beam arrange-
ments should use heterogeneity corrections during treat-
ment planning.
Another work done by Wang et al.[15] utilized MC calcu-
lation to evaluate the dosimetric effects of inhomogenei-
ties for five clinical lung and five H&N IMRT plans. The

IMRT plans were optimized using an in-house optimiza-
tion algorithm utilizing an equivalent path length-based
inhomogeneity correction and the plans were calculated
using an in-house pencil beam dose calculation algo-
rithm. All plans were recalculated with an EGS4-based MC
calculation algorithm. Although most of the dose-volume
indices calculated with both dose calculation algorithms
agreed well, there were >5% differences for some plans.
Another work done by Sakthi et al.[16] evaluated the
dynamic MLC IMRT dose-distributions calculated by the
Pinnacle
3
system's (Philips medical Systems, Milpitas,
CA) SC algorithm with EGS4-based MC calculations for
twenty-four H&N patients treated with the SIB IMRT tech-
nique. Their work showed that the flat phantom measure-
ments agreed much better with MC as compared to SC.
They also observed that although average SC-computed
doses in the patient agreed with MC-calculated doses, dif-
ferences >5% between the two algorithms were common.
They concluded that the inaccuracies in fluence prediction
were the major source of discrepancy.
A work by Leal et al.[21] investigated the use of MC for
routine IMRT verification. The IMRT plans were opti-
mized using Plato TPS (Veenendall, the Netherlands) and
the plans were recalculated using an EGS4-based MC sys-
tem for three cases, including two prostate and cavum.
The film dosimetry-based verification was also performed.
Major differences were found between MC and TPS calcu-
lated doses in situations of high heterogeneity.

A study by Francescon et al.[22] compared the differences
between step-and-shoot IMRT dose distributions calcu-
lated by the Pinnacle
3
system's (Philips medical Systems,
Milpitas, California, USA) collapsed cone convolution
Radiation Oncology 2009, 4:18 />Page 3 of 17
(page number not for citation purposes)
algorithm (version 6.0i) with EGS4-based MC calcula-
tions for one prostate and one H&N case. The BEAM [17]
MC code was utilized to simulate the particles through
MLC. They found that the dose differences at the isocenter
between Pinnacle
3
and MC calculations were 2.9% for
H&N plan and 2.1% for prostate plan. However, there
were up to 6% deviations for doses below 85% of the pre-
scription dose and even much higher deviations for doses
over the 85% of the prescription dose.
Another work done by Boudreau et al.[24] compared the
dose distributions calculated with the CORVUS finite size
pencil beam algorithm to the PEREGRINE MC dose calcu-
lations for eleven head and neck (H&N) patient treatment
plans. Their MC dose calculations, as compared to the
CORVUS pencil beam algorithm, showed that there was
an average reduction of 16% and 12% in the GTV and CTV
volumes covered by the prescription dose, respectively.
They concluded that the differences between the CORVUS
and PEREGRINE calculated doses were due to the lack of
secondary electron fluence perturbations which are not

modeled in the CORVUS, issues related to organ delinea-
tion near air cavities, and differences in reporting dose to
water versus dose to medium.
The use of an algorithm such as MC, which can explicitly
account for MLC leakage and scatter, can not only
improve dose calculation accuracy, but also reduce the
potential errors in the actually delivered dose to the
patients. Although many successful implementation of
MC in clinical settings have been previously reported [10-
25] none of these work reported the MC verification of
SIB-IMRT based prostate plans for a large set of patients.
The SIB-IMRT generated treatment fields often have large
intensity gradients which result in complex MLC leaf pat-
terns and presents challenges to conventional dose calcu-
lation algorithms. The purpose of this study is to evaluate
the dosimetric differences between Superposition/Convo-
lution (SC) and Monte Carlo (MC) calculated dose distri-
butions for twenty-two prostate patients treated with SIB
IMRT dose distributions. Furthermore, the SC and MC cal-
culated dose distributions were also compared to film-
based measurements performed in phantom. The results
of these comparisons will allow quantitative assessment
of the dosimetric accuracy of prostate patients treated with
SIB IMRT.
Methods
Patient Selection, Positioning and CT scanning
Twenty-two intermediate risk prostate cancer patients
with the pelvic lymph node involvement that were treated
with our in-house Internal Review Board-approved SIB
IMRT protocol were selected for this study. Patients were

CT scanned in a supine position with 3 mm slice thick-
nesses and slice separation using a Philips AcQsim scan-
ner (Philips Medical Systems, Cleveland, Ohio, USA).
Target volumes
The delineation of target(s) and critical structures for all
patients was done by a single physician with extensive
experience in the treatment of prostate cancer. For all
patients, the clinical target volume (CTV) included 2 cm
of seminal vesicles of the peri-prostatic rectum and a 5
mm expansion of the gross tumor volume (prostate only)
in all directions, except posteriorly. The prostate planning
target volume (PTV
prostate
) was generated expanding the
prostate CTV by a uniform 5 mm in all directions. The
nodal CTV included a 1 cm expansion of pelvic lymph
nodes in all directions excluding the anterior portion of 1
cm skin, prostate PTV, bladder, rectum, small bowel, and
bones. The nodal PTV volume (PTV
nodes
) was formed
expanding the nodal CTV by 5 mm in all directions
excluding prostate PTV and anterior skin 1 cm.
Critical Structures
The critical structures included rectum, bladder, small
bowel and femurs. Anterior portion of 1 cm skin region
was also contoured and included in the optimization to
limit dose to the anterior portion of patient's skin. In
addition, the unspecified tissue was also contoured and
included in the optimization.

IMRT Optimization and Treatment Planning
All IMRT plans were generated using seven equally-spaced
18 MV coplanar beams for dynamic delivery with the Var-
ian 21EX accelerator equipped with 120-leaf millennium
MLC. The choice of the beam arrangements was based on
the preliminary planning studies done for prostate IMRT
patients. The prescription doses to PTV
prostate
and PTV
nodes
were 61–63 Gy and 50.4 Gy respectively, delivered simul-
taneously in 28 fractions, following an upfront 6 Gy high
dose rate (HDR) brachytherapy. The Nominal Tumor
Dose (NTD) at 1.8 per fraction was 76 Gy assuming a α/β
= 3 for the prostate. The goal was to cover >97% of PTV-
prostate
with 61–63 Gy and >95% of PTV
nodes
with 50.4 Gy.
Dose-volume constraints for the critical structures were
summarized in Table 1.
Intensity modulation was achieved using the sliding win-
dow technique [26] which was implemented in the VCU
in-house IMRT optimization system. For the SC dose cal-
culation algorithm, the leaf positions (trajectories) are
converted into energy fluence transmission maps by using
an in-house analytic method that was based on the trajec-
tory-to-fluence algorithm [27]. The energy fluence trans-
mission maps were utilized to mainly attenuate the non-
modulated open field energy fluence, thereby resulting in

dose intensity modulation. The analytic algorithms often
use simplifications in describing the MLC leaf geometry
Radiation Oncology 2009, 4:18 />Page 4 of 17
(page number not for citation purposes)
when determining the MLC transmission factor and leaf-
end-modeling. This causes inaccurate representation of
the fluence modulation produced by the MLC. The ana-
lytic trajectory-to-fluence algorithm utilized in this work
included the average rounded leaf-tip transmission,
which was determined from published MC simulation
work, thus including head-scattered photons in the leaf-
tip transmission and source size effects and also MC-
derived term[11] that accounts for the scattered photons
initiating from the MLC leaves. The in-house leaf sequenc-
ing method used for the SC algorithm in this work is also
the basis of the dynamic MLC implementation in the
Pinnacle
3
IMRT software module (7.4 and higher ver-
sions). The details of the leaf-sequencing method have
been described in the literature[16,25,28].
During IMRT optimization, dose calculation was done
using the SC algorithm available in Pinnacle
3
, with the
intensity modulation determined as a transmission com-
pensator matrix which was imported from the VCU IMRT
optimization system. The optimized transmission com-
pensator matrix, then, converted into a MLC leaf sequence
as deliverable MLC transmission compensator matrix,

which approximately accounts for the head-scatter, inter-
leaf and intra-leaf leakage effects on the energy fluence.
The deliverable fluence matrix, then, loaded into the Pin-
nacle TPS and the dose (caused by that energy fluence)
within the patient was computed by the Pinnacle's SC
algorithm.
The VCU in-house IMRT optimization system used in this
study was interfaced with the Philips Pinnacle
3
TPS
(Philips Laboratories, Milpitas, California, USA), that is
used for contouring, beam placement, isodose display,
and plan evaluation. The IMRT optimization system
employed a gradient-based search algorithm and
described in detail elsewhere [29]. The Pinnacle's adaptive
SC dose calculation algorithm, including heterogeneity
corrections, which was based on the work done by Mackie
et al.[4,30], was used during both optimization and final
dose calculation stage after MLC leaf sequencing was per-
formed. Our numerical experiments did not find any dif-
ference between Pinnacle's collapsed-cone and adaptive
SC results and therefore, adaptive SC was used for treat-
ment planning of all clinical patients. The adaptive SC
dose calculation algorithm model consists of 1) modeling
the incident energy fluence as it exits the accelerator head,
2) projection of this incidence energy fluence through a
density representation of a patient to compute a total
energy released per unit mass (TERMA), and 3) 3-D super-
position of the TERMA with an energy deposition kernel
to compute the dose. The algorithm also uses a ray-tracing

during superposition to incorporate the effects of the het-
erogeneities to the lateral scatter. The Pinnacle's adaptive
SC beam model parameters characterize the radiation
exiting the head of the linear accelerator by the starting
point of a uniform plane of energy fluence describing the
intensity of the radiation. The algorithm, then, adjusts the
fluence model to account for the flattening filter, collima-
tors and beam modifiers. The SC beam modeling requires
the measurements of the depth dose curves (the energy
spectrum determination), dose profiles (incident energy
fluence determination inside the field), dose profiles
extending outside the field (scatter dose determination
from the machine head components), calibration and rel-
ative output factors. The initial energy spectrum for 6 MV
and 18 MV photon beams was chosen from a library of
spectrums available in Pinnacle
3
beam modeling module.
The dose calculation grid for each IMRT patient plan
included the entire patient CT data set and was 4 mm in
each Cartesian coordinates. The adaptive SC algorithm
was commissioned to match measurements, and the
agreement between the measurements and the adaptive
SC were generally within ± 2% or 2 mm for both open and
MLC-defined fields. The Pinnacle
3
beam modeling meas-
urements were performed in a Wellhofer 48 cm × 48 cm ×
48 cm water phantom (IBA Dosimetry, Bartlett, Tennes-
see, USA) for field sizes ranging from 1 cm × 1 cm to 40

cm × 40 cm. The measurements of 5 cm × 5 cm to 40 cm
× 40 cm field sizes were performed using Wellhofer IC-10
(0.1 cm
3
active volume). For the measurements of small
field sizes of 1 cm × 1 cm to 4 cm × 4 cm, Wellhofer IC-3
chamber (0.03 cm
3
active volume) were used.
Monte Carlo Dose Verification
SIB IMRT plans for each patient in this study were recom-
puted with MC to investigate the accuracy of the SC algo-
rithm which was coupled with our in-house SC fluence
Table 1: Dose-volume constraints used in IMRT optimization and
plan evaluations for twenty-two prostate patients.
Structures Limiting Dose(Gy) Volume Constraint (%)
PTV 61–65 97
70 1
PTV
Nodes
50.4 95
60 5
Femurs (L&R) 35 50
40 10
45 2
Rectum 45 50
60 10
65 2
Bladder 45 50
60 10

65 2
Small Bowel 25 50
45 10
50 2
Skin 1 cm Ant 45 2
30 20
Radiation Oncology 2009, 4:18 />Page 5 of 17
(page number not for citation purposes)
modulation prediction algorithm. MC dose recalculation
for each patient was performed using the same leaf
sequence files and monitor units (MUs) that were
obtained using SC based optimization. Hence, the MC
results were computed in terms of dose per MU and the
MUs used for the patient's treatment were the ones used
for the dose evaluation. The SC method (as described in
IMRT optimization and Treatment Planning section) con-
verts the MLC leaf sequencing file into a virtual compen-
sator to perform the IMRT calculations, whereas the MC
method uses the MLC leaf sequencing file directly. The
strength of MC-based methods stems from the fact that it
can realistically model radiation transport and interaction
process through the accelerator head, beam modifiers and
the patient geometry [10]. Specifically, the MC calculation
algorithms can include the detailed description of the
MLC leaf geometry and directly consider the effect of the
MLC on the primary and scatter beam fluence on a parti-
cle-by-particle basis. The implementation of the MC algo-
rithm used in this work was described in detail
elsewhere[16,25], but is briefly summarized here for com-
pleteness. Our MC dose calculations were based on EGS4

code [31], along with user codes BEAM [17] and DOSXYZ
[32]. The accuracy of EGS4 code, along with user codes
BEAM and DOSXYZ, for both homogeneous and hetero-
geneous phantoms have been extensively tested by other
investigators [12,19,33] hence will not be discussed here.
The MC simulations were run on a dedicated dual-proces-
sor Beowulf cluster, containing ten 2.4- to 2.8-GHz dual-
processor nodes. MC algorithm is interfaced to Pinnacle
3
TPS such that an integrated control interface directly reads
gantry angles, jaw positions, beam energies, and patient
CT densities from the Pinnacle
3
TPS. Particles in each
beam during MC simulation were read from a previously-
commissioned phase-space that includes particle posi-
tions, directions, and energies exiting the treatment head
which are incident upon the MLC using BEAM [17],
through the dynamic MLC using an in-house code [10],
and through the patient using DOSXYZ [32], where
deposited energy was scored. In the MC MLC model, the
MLC was divided into simple geometric regions where the
simplified radiation transport can be performed. For pho-
ton beams, the MC MLC model predicted both beam
hardening and leaf-edge effects (tongue-and-groove) and
included attenuation and first Compton scatter interac-
tions. The MLC leaf positions were directly read from the
MLC leaf sequence files that are generated by the IMRT
optimization system. The positions in the leaf sequence
files were then translated into physical MLC leaf tip posi-

tions at the MLC plane using a look-up-table and demag-
nification from the machine mlctable.txt file. After the
MLC leaf tip positions, as a function of monitor units, are
determined, the particles were transported from the
phase-space of particles leaving the treatment machine
jaws and the particles were transported through the MLC
leaves. The particles exiting the MLC were written into a
phase-space file which was used as an input for MC
patient dose calculation. The MC MLC method summa-
rized here was tested for both 6-MV and 18-MV photon
beams and the details of this method have been reported
in the original paper by Siebers et al. [10].
For MC calculations, the dose calculation grid for each
patient included the entire patient CT data set and was 4
mm in each x, y, and z Cartesian coordinates. For each
beam, a nominal value of ~2% statistical uncertainty at a
depth of D
max
was used for all MC dose calculations, lead-
ing to a 1% overall statistical uncertainty from all treat-
ment beams in the dose to the target structures. It has been
previously shown that an overall 2% statistical uncer-
tainty in MC calculations has minimal effect on DVHs
[12,34]. Structure-by-structure analysis of the statistical
uncertainty in the dose to the critical structures was <1.5%
respectively [35] for the prostate cases included in this
study. The uncertainty in DVH-evaluated parameters,
however, was <1.0%. Pinnacle SC dose calculation algo-
rithm utilized in this work reports absorbed dose to water.
The MC dose calculation algorithm, on the other hand,

inherently reports absorbed dose to medium. For consist-
ency with SC calculations, the MC calculated dose distri-
butions were converted from dose-to-medium to dose-to-
water using the post MC-calculation methods described in
Siebers et al.[36]
The MC dose calculation algorithm used in this work has
been commissioned to match measurements and has
been thoroughly tested and benchmarked against meas-
urements for both 6-MV and 18-MV photon beams. The
details of the MC commissioning can be found in the ref-
erences [10-12,37-39]. The agreement between our MC
dose calculation with the measurements for both open
and dynamic MLC-defined fields was found to be gener-
ally within ± 1% or 1 mm [10,12], except in the build-up
region and for very small sliding window DMLC fields
(0.5 cm) where there were disagreements up to 1.5% (for
6 MV) and 2.5% (18 MV) between MC calculated and the
measured doses.
Comparison with Film Measurements
In addition, the patient plans that were initially planned
and treated using VCU IMRT system were experimentally
verified beam-by-beam using film dosimetry as part of the
routine IMRT QA. The verification of each dose calcula-
tion algorithm for each treatment beam (verification of
the IMRT fluence estimation) was quantified by perform-
ing dose calculations using both SC and MC algorithms in
a flat water phantom. The SC and MC calculated dose dis-
tributions results were compared to EDR2 film measure-
ments (Eastman Kodak, Rochester, New York, USA)
performed at a 5 cm depth in a 30 cm × 30 cm × 20 cm

Radiation Oncology 2009, 4:18 />Page 6 of 17
(page number not for citation purposes)
solid water phantom. For EDR2 film measurements for
each plan, the gantry angles were set to zero (Varian sys-
tem) with a source to film distance 100 cm. The film
measurements utilized the same MLC leaf sequence files
that were used for the patient IMRT treatment as well as
used in SC and MC recalculation.
For each patient treatment plan, the film calibration
curves were generated by irradiating films, placed at d
max
,
100 cm SSD, 10 × 10 cm field (where 1 MU = 1 cGy), with
0 to 300 MUs in increment of 10 MUs. EDR2 films used
for the measurements of treatment and calibration films
came from the same batch. All films for each plan were
processed the day of irradiation and scanned using the
VIDAR VXR 16 (Vidar Systems Corporation, Herndon,
Virginia, USA) and were analyzed with an in-house devel-
oped scanning software. The reproducibility of the films
was within 0.5%. The measured dose distributions of each
beam were superimposed with the SC and MC calculated
dose distributions and the parameters such as gamma
index, dose difference, distance-to-agreement (DTA) were
calculated using an in-house software developed based on
the published work by Low et al.[40] and Harms et al.[41]
For each plan, both the 2% dose difference, 2 mm DTA
criteria and the 3% dose difference, 3 mm DTA criteria
were used for the calculation of the fraction of points pass-
ing with gamma (γ) index <1.

Plan evaluation
For each patient plan, the SC and MC calculated patient
plans were evaluated using dose-volume-based indices
(see Table 1). For the PTV
prostate
, the minimum dose
received by 98% of the volume (D
98
), the maximum dose
received by 2% of the volume (D
2
), the dose received by
50% of the volume (D
50
) and the mean dose (D
mean
) were
evaluated. For PTV
nodes
, the minimum dose received by
95% of the volume (D
95
), D
50
and D
2
were evaluated. For
critical structures, D
2
, D

10
, D
50
indices were evaluated. The
D
2
index was used as a surrogate to evaluate the maximum
dose since in some plans, volumes of only a very small
number of voxels received higher or lower doses and this
overstated the absolute maximum and minimum dose,
and could bias the data. Furthermore, the D
2
is less prone
to the statistical fluctuations in MC methods [16].
The dose-volume constraints for all structures defined in
Table 2 were used for the evaluation of all plans. In addi-
tion, the homogeneity index (HI), which was defined as
the [(D
98
- D
2
)/D
presc
.], was calculated for PTV
prostate
. The
comparisons between SC optimized and MC re-calculated
plans were made relative to the SC calculated plans using
Table 2: Summary of results for twenty-two prostate plans, showing average relative % differences between SC and MC calculated
dose-volume indices including standard deviation and the range of dose-volume indices.

Structure Dose-volume index Range of Indices (%) Average relative % difference
PTV
prostate
D
98
[-3.8, 0.06] 1.2 ± 1.1 (p < 0.05)
D
50
[-3.2, 0.9] 0.8 ± 1.0 (p < 0.05)
D
2
[-0.5, 4.2] 1.4 ± 1.4 (p < 0.05)
D
mean
[-2.5, 2.9] 0.3 ± 1.1 (p > 0.05)
PTV
nodes
D
95
[-4.7, 0.6] 1.5 ± 1.4 (p < 0.05)
D
50
[-3.0, 1.0] 0.4 ± 1.3 (p > 0.05)
D
2
[-2.1, 2.6] 0.1 ± 1.1 (p > 0.05)
D
mean
[-2.9, 1.4] 0.3 ± 1.2 (p > 0.05)
Rectum D

50
[-3.6, 3.4] 0.2 ± 1.8 (p > 0.05)
D
10
[-2.9, 2.2] 0.3 ± 1.5 (p > 0.05)
D
2
[-2.5, 1.9] 0.4 ± 1.2 (p > 0.05)
D
mean
[-2.9, 3.1] 0.6 ± 1.7 (p > 0.05)
Bladder D
50
[-3.7, 1.4] 0.9 ± 1.4 (p < 0.05)
D
10
[-3.2, 1.5] 0.7 ± 1.3 (p < 0.05)
D
2
[-2.7, 2.2] 0.7 ± 1.1 (p < 0.05)
D
mean
[-3.8, 0.9] 0.8 ± 1.3 (p < 0.05)
Small Bowel D
50
[0.2, 115] 30.2 ± 40.7 (p < 0.05)
D
10
[-3.1, 120.7] 10.1 ± 26.4 (p < 0.05)
D

2
[-2.6, 100.1] 6.8 ± 21.5 (p < 0.05)
D
mean
[-1.5, 123.5] 16.5 ± 27.5 (p < 0.05)
Femurs D
50
[1.2, 14.5] 8.6 ± 3.6 (p < 0.05)
D
10
[-3.0, 8.4] 4.6 ± 3.5 (p < 0.05)
D
2
[-2.9, 8.6] 4.1 ± 3.3 (p < 0.05)
D
mean
[-0.9, 10.3] 6.3 ± 3.9 (p < 0.05)
Skin 1 cm Ant D
2
[1.7, 12.1] 7.7 ± 3.8 (p < 0.05)
The p values determine if the mean of relative differences are significantly different from zero.
Radiation Oncology 2009, 4:18 />Page 7 of 17
(page number not for citation purposes)
a paired two-tailed student's t-test. The average values of
the dose-volume indices were found to be statistically sig-
nificant if p value ≤ 0.05. For each patient, differences
between the SC and MC re-calculated plans were calcu-
lated with respect to the local point of interest using the
formula:
where x is a particular dose-volume index and SC and MC

are the techniques being evaluated. The comparisons were
made relative to the SC calculated plans since these plans
were used for the patient treatments.
In addition to dose-volume indices, the SC- and MC-cal-
culated 3D dose distributions were compared using the
3D gamma analysis [42] with the gamma criteria of 3%
dose difference and 3 mm DTA. The MC dose calculation
was used as the reference dose for the 3D gamma analysis.
For both SC and MC dose calculations, the dose calcula-
tion grid size was set to 0.4 cm × 0.4 cm × 0.4 cm. For 3-
D gamma index calculation, the dose values were interpo-
lated linearly at a sample step size of 0.02 cm. The maxi-
mum search distance was set to 1.0 cm. When a sample
step size of 0.02 cm was used during the linear interpola-
tion, the differences in the percentage of the points passed
the gamma criteria was very negligible for the dose calcu-
lation grid sizes of 0.4 cm, 0.3 cm and 0.2 cm. This is also
consistent with the results presented at the work done by
Wendling et al. [42]. For each structure, the gamma values
averaged over all patient population were computed.
Relative difference% =
−
×
D
x
MC
D
x
SC
D

x
SC
100
Gamma analysis that compares SC and MC algorithms with measured dose distributions in flat phantom for 11 of patient plansFigure 1
Gamma analysis that compares SC and MC algorithms with measured dose distributions in flat phantom for
11 of patient plans. The percentage of points failed was averaged over all of the fields for each patient for γ >1 with 2% tol-
erance and 2 mm DTA. The agreement of MC results is better than SC.
Radiation Oncology 2009, 4:18 />Page 8 of 17
(page number not for citation purposes)
Results
Monte Carlo Verification of Film Measurements
Figure 1 summarizes the gamma analysis of eleven of the
patient plans (included the ones with the highest and low-
est percentage of points failed gamma criteria) by compar-
ing the phantom measured dose distributions with SC
and MC calculated dose distributions. In Figure 1, the per-
centage of points failed gamma test were performed aver-
aged over all of the plan's treatment fields for each patient
with γ >1 with 2% tolerance and 2 mm distance to agree-
ment (DTA). The results demonstrate that the average of
patient plans with percentage of points failing gamma test
is 8.1% ± 3.8% for MC (ranging from 4.3% to 18.4%) and
16.7% ± 5.7% for SC (ranging from 10.9% – 30.7%). For
a more commonly used clinical gamma criteria of 3%/3
mm, the average of patient plans with percentage of
points failing gamma (γ >1) was 2.6% ± 1.6% for MC
(ranging from 1.3% to 5.7%) and 5.2% ± 3.8% for SC
(ranging from 2.0% – 12.6%).
Figure 2a–c shows gamma analysis comparing the meas-
ured dose distribution with the SC and MC calculated

dose distributions in flat phantom for one of the patient
treatment fields (180° angle). The percentage of points
passed for γ <1 with 2% tolerance and 2 mm DTA was
91.2% with MC (Figure 2c) as compared to 84.3% for SC
(Figure 2b).
Monte Carlo Verification of Patient Plans
Figure 3a–d shows the comparison of SC and MC calcu-
lated transverse-slice isodose distributions and the corre-
sponding absolute dose differences between the two dose-
calculation algorithms. Also shown, are the DVHs for one
of the patients (Patient 12) included in this study.
While the approved plan (SC calculated) for this patient
delivered 62 Gy to 98% of the PTV
prostate
, the MC re-com-
puted PTV D
98
predicted 61.2 Gy (1.39% lower than the
predicted by the SC). The MC predicted PTV
prostate
D
50
was
also 1.6% lower than the one predicted by SC. For PTV
n-
odes
, the MC predicted D
95
(47.9 Gy) was 3.5% lower than
the one predicted by the SC (49.7 Gy). The value of this

index was slightly higher than our clinically acceptable
tolerance level of 3%. The HI for PTV
prostate
increased from
10.1 with SC to 11.6 with MC for this patient. For critical
structures, the MC also predicted lower doses for each
dose-volume index. These differences were <3.5%. With
the exception of the PTV
nodes
, the differences between SC
and MC predicted dose-volume indices for all PTVs and
critical structures were in general within our clinically
acceptable tolerance level of 3%. Figure 3c displays the
absolute dose difference between the SC and MC calcula-
tion algorithms on a transverse plane for Patient 12. The
range of dose differences between the two calculation
methods varied from -8.9 Gy to +5.0 Gy, showing greater
positive deviations in regions close to the patient skin,
and in regions where heterogeneity structures (e.g., bone,
air) and also where large intensity gradients present. The
greater positive deviations in skin were due to less accu-
rate prediction of surface doses and doses in build-up
region by SC algorithm as compared to MC algorithm.
Figure 3d displays the DVHs calculated with SC (solid
lines) and MC (dashed lines) for this patient, showing
lower MC doses for all structures.
Dosimetric results for all patients are summarized in
Table 2, which shows the average relative % differences
with their standard deviations and ranges for the dose-vol-
ume indices for twenty-two IMRT patient plans. On aver-

age, MC predicted doses for PTV
prostate
and PTV
nodes
were
lower than the ones predicted by SC, indicating ~1.6%
systematic difference in the SC calculated dose. Although
the average relative % difference between SC and MC cal-
culated D
98
, D
50
, D
2
and D
mean
indices for PTV
prostate
is less
than 1.5%, there were deviations up to 4.2% in the
regions of prostate PTV extending to the bone, in individ-
ual patient plans (Patient 15). Similarly, although the
average differences in SC and MC calculated dose-volume
indices for PTV
nodes
were less than 2%, deviations as high
as -4.7% (Patient 11) in areas where the PTV
nodes
volume
extending to the anterior portions of the skin region. For

both rectum and bladder, the average relative % differ-
ences for all dose-volume indices were less than 1%; how-
ever, differences up to 3.8% in bladder D
mean
were
observed in some patients (Patient 8). The reason for this
large difference in bladder D
mean
may be due to the large
air pocket in the bladder of this patient which was intro-
duced by pulling of the foley catheter before the CT scan-
ning. The largest differences between SC and MC
computed doses were observed in small bowel and femur.
Differences of 0.2% to 115% in small bowel D
50
(e.g.;
Patient 14) and 1.2% to 14.5% in femurs D
50
were
observed. The large deviations in small bowel doses was
due to large differences in small bowel volume within the
treatment field and very small doses received by the small
bowel for some patients. Since the small bowel volume
for Patient 14 was very small (14 cc) and was far from the
high dose regions, it received much lower doses as com-
pared to the other patients (e.g; D
50
= 104.7 cGy with SC
vs. 225.1 cGy with MC). Therefore, the observed large dif-
ferences are as a result of the large MC statistical uncer-

tainties in this lower dose region.
For majority of patients, large differences in SC and MC
calculated dose-volume indices for femurs may be due to
the systematic errors introduced when converting from
dose-to-medium to dose-to-water in MC-calculated IMRT
treatment plans. For a previously done study on these
prostate IMRT patients [35], for femoral heads, the sys-
tematic shifts of ranging from 4.0% to 8.0% in dose-vol-
Radiation Oncology 2009, 4:18 />Page 9 of 17
(page number not for citation purposes)
(a) The measured dose distribution and gamma analysis that compares with the (b) SC and (c) MC calculated dose distributions in flat phantom for one of the patient treatment fieldsFigure 2
(a) The measured dose distribution and gamma analysis that compares with the (b) SC and (c) MC calculated
dose distributions in flat phantom for one of the patient treatment fields. The percentage of points passed was cal-
culated for γ <1 with 2% tolerance and 2 mm DTA. The agreement of MC results is better than SC. Note that the color bar in
(a) represents the measured dose in cGy, whereas the color bar in (b) and (c) shows the range of γ values.
Radiation Oncology 2009, 4:18 />Page 10 of 17
(page number not for citation purposes)
Comparison of (a) SC- and (b) MC-calculated isodoses on transverse CT slice, (c) colorwash of absolute dose differences between two methods and d) DVHs for Patient 12 included in this studyFigure 3
Comparison of (a) SC- and (b) MC-calculated isodoses on transverse CT slice, (c) colorwash of absolute dose
differences between two methods and d) DVHs for Patient 12 included in this study.
Radiation Oncology 2009, 4:18 />Page 11 of 17
(page number not for citation purposes)
ume indices were observed when using dose-to-water vs.
dose-to-material. This systematic shift for femurs was due
to the high calcium content of the bones, which increased
the water-to-material stopping power ratio due to the
increased neutron/proton ratio in calcium relative to
water (caused by the hydrogen content of water).
Figure 4 graphically illustrates the patient-to-patient vari-
ation in relative percent deviations between SC and MC

computed PTV
prostate
D
98
and PTV
nodes
D
95
indices. The
deviations in PTV
nodes
D
95
were greater than PTV
prostate
D
98
and the MC calculated doses were lower than the ones ini-
tially predicted by SC, except for two patients.
Figure 5 shows the patient-to-patient variation in SC and
MC calculated homogeneity index (HI). For all plans, MC
recalculated plans were less homogeneous than the
planned SC dose distributions (average HI: 9.9% ± 2.1 for
SC vs. 12.8 ± 2.8 for MC). The largest difference in HI
between the SC and MC calculated plans was found to be
for Patient 10 (13.2 for MC vs. 7.7 for SC).
Figure 6 shows the percent deviations between SC and MC
computed bladder, rectum and small bowel D
50
for all

patients. Although the deviations for both rectum and
bladder were on both sides of the norm, the MC predicted
doses were lower for the majority of the patients (15 out
of 22) for the bladder and MC doses were higher for the
majority of the patients (14 out of 22) for the rectum. The
deviations between SC and MC computed dose-volume
indices for bladder and rectum were within 3% for the
majority of the patients. This is within our clinical accept-
Relative percent differences between SC and MC calculated plans for the PTV
prostate
D
98
and PTV
nodes
D
95
Figure 4
Relative percent differences between SC and MC calculated plans for the PTV
prostate
D
98
and PTV
nodes
D
95
. MC
predicted doses are lower than SC doses for all, except for two patients. Note that relative % difference =
where x is a specified dose-volume index.
D
x

MC
D
x
SC
D
x
SC
−
×
⎛
⎝
⎜
⎜
⎞
⎠
⎟
⎟
100
Radiation Oncology 2009, 4:18 />Page 12 of 17
(page number not for citation purposes)
ance criterion for our clinical IMRT patient dose verifica-
tion protocol (≤ 3%). However, up to 6.5% differences
were observed for some patients. For all patients, the per-
cent deviations in small bowel D
50
were positive, indicat-
ing that MC predicted doses were higher. However, the
dose-volume criteria were still clinically acceptable by the
physician since D
50

values were within or well below the
tolerance doses.
The evaluation of the number of patient plans that satis-
fied a given dose-volume criteria is presented in Table 3.
For majority of the plans, the SC and MC calculated dose-
volume indices agreed within 3% for both target and crit-
ical structures. For example, twenty out of twenty-two
patients had the SC and MC calculated indices agree
within 3% for both PTV
prostate
D
98
and eighteen out of
twenty-two plans agreed within 3% for PTV
nodes
D
95
. If we
considered all of the dose-volume indices for all target
structures (D
98
, D
95
, D
90
, D
50
and D
2
), sixteen out of

twenty-two patients were within 3% criteria. The rest of
the plans were within 5% criteria. Although the similar
agreement was observed for rectum, bladder and small
bowel, five out of twenty-two plans exceeded the D
50
= 25
Gy criteria for small bowel. However, the physician still
considered these plans clinically acceptable.
Table 4 summarizes of the average percentage of points
passing the 3D gamma evaluation for the PTV, PTV
prostate
,
rectum, bladder, small bowel and femurs and the total 3D
dose distribution. While for PTV
prostate
, PTV
nodes
, bladder
and rectum, the average gamma scores were >95.0%, they
were 90.2% for small bowel and 91.6% for femurs. The
lower 3D gamma pass rates for small bowel and femurs
are consistent with the large differences observed in dose-
volume indices between MC- and SC-calculated doses.
Prostate PTV homogeneity indices (HI) with SC and MC computed IMRT plans for all patientsFigure 5
Prostate PTV homogeneity indices (HI) with SC and MC computed IMRT plans for all patients.
Radiation Oncology 2009, 4:18 />Page 13 of 17
(page number not for citation purposes)
Discussion
This study of twenty-two prostate patient cohort treated
with SIB IMRT showed differences between the SC and

MC predicted doses. Film dosimetry results confirmed
that the MC algorithm predicted flat-phantom measure-
ments better than the SC algorithm (Figure 1). Improved
MC calculated dose agreement was due to the superior flu-
ence prediction by the MC algorithm. For each patient
plan, the deviations of the phantom and patient data sets
for each case were found to be on the same side of the
mean, demonstrating that the deviations observed in flat-
phantom are representative of the deviations in the
patients.
For individual patient plans, our results showed that the
MC-predicted doses for PTV
prostate
and PTV
nodes
were up to
3.8% and 4.7% respectively lower than the ones predicted
by the SC algorithm. For some patients, the magnitude of
such deviations might decrease the intended target dose
levels that are required for the treatment protocol, placing
the patients in different dose levels that do not satisfy the
protocol dose requirements. For rectum and bladder, the
differences in SC and MC predicted doses were less than
3% for the most of the patient plans although deviations
as large as 3.8% were seen some individuals. For femoral
heads, the deviations between SC and MC predicted doses
reached 14.5%. This appeared to be related to the calcium
content of the bones.
Relative percent differences between SC and MC calculated plans for the bladder, rectum and small bowel D
50

Figure 6
Relative percent differences between SC and MC calculated plans for the bladder, rectum and small bowel D
50
.
Relative percent differences between SC and MC calculated plans for the bladder, rectum and small bowel D
50
. Note that rela-
tive % difference = .
D
MC
D
SC
D
SC
50 50
50
100
−
×
⎛
⎝
⎜
⎜
⎞
⎠
⎟
⎟
Radiation Oncology 2009, 4:18 />Page 14 of 17
(page number not for citation purposes)
In this work, differences between SC and MC predicted

mean doses (D
mean
) for the PTV
prostate
were similar (<3.0%
with an average of 0.3) to those reported by Yang et
al.[14], whose work showed that the differences in mean
dose to the prostate CTV were within 3%. In our study,
differences in maximum dose (D
2
was used as a surrogate)
to the rectum and the bladder for all cases were ≤ 2.7%
(with an average of 0.38% and 0.0.72% respectively). In
contrast, the differences in maximum dose to the rectum
in study by Yang et al.[14] were ≤ 4%. Other differences
exist between these two studies. Yang et al.[14] used a step
and shoot technique whilst the dynamic MLC (sliding
window) technique was used in this work. Lastly, their
work focused on the investigation of the effects of hetero-
geneities on dose distributions estimated by both pencil
beam and MC algorithms using both coplanar and non-
coplanar beams. Any of these factors could account for the
differences observed in our study. Our work supports the
fact that the dose prediction errors vary significantly
(>3%) from patient to patient, suggesting that individual
patient evaluation is indicated.
In this work, possible causes of differences between the SC
and MC predicted doses were likely due to the inaccurate
prediction of the beam fluences, imprecise handling of
heterogeneities in patient, and differences in beam mode-

ling for SC and MC dose calculation algorithms. A work
by Mihaylov et al.[28] found that the differences in the
DMLC modeling between MC-based methods and the
commercial algorithms were main causes of dosimetric
differences. Hence, we speculate that the main contribut-
ing factor to the dosimetric differences observed in this
study is in the MLC transport rather than transport in the
patient. In this work, for the highly modulated SIB IMRT
prostate cases involving two targets (PTV
prostate
and PTV
n-
odes
) and critical structures, and our in-house analytic flu-
ence prediction algorithm together with the Pinnacle's
adaptive SC overestimated the MLC transmission, scatter
and leakage, resulting in larger doses to both PTV
prostate
and PTV
nodes
as compared the doses calculated by MC
algorithm, resulting in negative dose prediction errors.
The MC algorithm used in this work inherently included
the leaf scatter, tongue-and-groove, and beam hardening
effects on the fluence upon the patient. In the SC dose-cal-
Table 3: Number of patient plans satisfying a given dose-volume criteria for target and critical structures.
Indices Criteria Number of Plans
Relative % difference
PTV
prostate

D
98
<3% 20
>3%, <5% 2
PTV
prostate
D
50
<3% 21
>3%, <5% 1
PTVN
nodes
D
95
<3% 18
>3%, <5% 4
PTVN
nodes
D
50
>3% 21
>3%, <5% 1
Bladder D
50
<3% 19
>3%, <5% 3
Bladder D
2
<3% 22
Rectum D

50
<3% 19
>3%, <5% 3
Rectum D
2
<3% 22
Small Bowel D
50
<10% 5
exceeding 25 Gy due to deviation >10% 1
Small Bowel D
2
<3% 17
>5%, <10% 1
>10% 4
Table 4: Gamma index values averaged over all patient population for each structure and 3D total dose with gamma criteria of 3%
dose difference and 3 mm DTA.
PTV
prostate
PTV
nodes
Rectum Bladder Small Bowel Femurs Total 3D dose
Fraction of points pass γ (%) 96.0 ± 4.4 96.1 ± 3.9 97.7 ± 2.7 96.5 ± 4.8 90.2 ± 9.7 91.6 ± 5.7 94.3 ± 1.9
Note that The MC dose calculation was used as the reference dose for the 3D gamma analysis.
Radiation Oncology 2009, 4:18 />Page 15 of 17
(page number not for citation purposes)
culation algorithm, however, the intensity modulation
was incorporated into dose computation using a trans-
mission-compensator matrix during the estimation of flu-
ence upon the patient. The MLC leaf scatter, tongue-and-

groove and beam hardening effects were approximately
incorporated into the fluence modulation. The fluence-to-
trajectory estimation that is more accurate than the one
used for this work would improve the results of SC calcu-
lation of prostate SIB IMRT patients. The dynamic MLC
leaf-sequencing technique utilized in this work is the basis
of the sliding window technique used in Pinnacle
3
IMRT
software (7.4 and higher versions) that includes the
tongue-and-groove effect. Mihaylo et al.[28] compared
the in-house fluence estimation used in this work with the
one used in Pinnacle
3
dynamic MLC IMRT module, and
his gamma analysis with ≤ 2%/2 mm criteria revealed that
the results of both methods agreed within 1%.
The SC and MC algorithms used in this work utilized dif-
ferent fluence prediction models although both SC and
MC algorithms[10,37] have been extensively tested and
commissioned to match the measurements. Therefore, the
use of these different beam models might explain the dose
differences between SC and MC calculations. The contri-
bution of use of different fluence prediction algorithms to
the dose prediction errors is not included in this work and
is the subject of a future study. One possible solution to
avoid the differences introduced due to different fluence
prediction models is to use the MC to predict the energy
fluence and to use the SC to predict the dose to the
patient. A work done by Mihaylov et al. [28] introduced a

hybrid method, which utilized a MC code, to predict the
energy fluence modulation incident upon a patient, and a
conventional dose calculation algorithm (SC) to estimate
the resultant dose within the patient. All computational
methods used in their work use the same dose calculation
algorithm to predict the dose to the patient, thereby iso-
lating the effect of the prediction of the incident energy
fluence which is inherent to the dose calculation algo-
rithm used. They benchmark their method by comparing
in-phantom measured dose distributions with analytic
methods (the one used in our work), including the one
implemented in Pinnacle
3
TPS. Their work showed that
the hybrid method better predicts the measurements and
also shows that the major factor causing the differences
between the SC and MC was the estimation of the fluence
upon the patient.
In Figure 3a–b and 3d, the effect of heterogeneities for
Patient 12 is not very apparent. There was 3.4% decrease
in maximum dose predicted by MC to the femoral heads
for this patient, for some patients, however, there were up
to 8.6% increase in D
2
predicted by MC to the femoral
heads, indicating that the heterogeneous patient medium
can be a potential source. Dose deviations due the frac-
tional contribution of patient heterogeneities for prostate
SIB IMRT patients are the subject of a future study. A work
by Mihaylov et al. [43] quantified the contribution of

patient heterogeneities for head and neck SIB IMRT
patients. Their work demonstrated that the effect of SC-
modeled tissue heterogeneities were < ± 3% for 98.3% of
the dose-volume indices used for the evaluation.
Differences between SC- and MC-calculated prostate SIB
IMRT dose distributions indicates that the SC calculated
treatment plans contain optimization convergence
errors[25]. Clinically, the optimization convergence
errors cause a suboptimal plan to be delivered to a patient.
These errors may be reduced using a more accurate dose
calculation algorithm (e.g.; MC) during treatment plan
optimization, resulting in highly accurate dose distribu-
tions. A work done by Dogan et al. [25] showed that forty
percent of the head and neck IMRT patients exhibited a
convergence error of 5% in at least one DVH endpoint.
Figure 5 shows that the MC-recalculated IMRT plans were
less homogeneous. This might be due to the fact that the
SC was used during the optimization. The homogeneity
would have been improved if MC was used during the
optimization.
The MC recomputed SIB prostate IMRT treatment plans
showed that patient target doses were less than the one
predicted by the treatment planning system's SC algo-
rithm. For some cases, the MC recalculated doses were less
than the SC calculated doses by nearly 2 Gy for all dose-
volume indices. The deviations of this magnitude in target
doses clearly indicate that the required target volume cov-
erage by the protocol was not achieved for some patients
and this may have clinical implications.
The MC dose verification tool presented in this work is

fully integrated with our treatment planning system and
showed itself to be a reliable tool to verify each of the clin-
ical IMRT treatment plans. Furthermore, different from
conventional dose verification methods, this tool allows
assessment of dose distributions within the patient.
Conclusion
MC dose calculation was used to recalculate dose distribu-
tions for twenty-two SIB-IMRT prostate plans that was
originally optimized and calculated with SC. Measure-
ments in phantom confirmed that MC agreed better with
film measurements than the SC. Differences between the
MC and SC computations in patient plans are likely to
arise due to the errors in fluence prediction, photon leak-
age through patient, and photon transport through MLC
leaves for SC based calculations. It is important to investi-
gate the contribution of each error and determine the
exact causes of these deviations between SC and MC cal-
culated doses since this may have clinical implications.
Radiation Oncology 2009, 4:18 />Page 16 of 17
(page number not for citation purposes)
These results demonstrate that MC clearly can play an
important role in SIB IMRT treatments. The results of this
study and those of other treatment sites have resulted in
the implementation of MC-based IMRT at our institution.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
All authors read and approved the final manuscript.
ND designed the study and performed the MC simula-
tions and data analysis, and revised the manuscript. IM

participated in data analysis. YW participated in data col-
lection and revised the manuscript. PJK participated in
design of the manuscript and revised the manuscript. JVS
participated in design of the manuscript and revised the
manuscript. MPH participated in data collection and
revised the manuscript.
References
1. Wu Q, Manning M, Schmidt-Ullrich R, Mohan R: The potential for
sparing of parotids and escalation of biologically effective
dose with intensity-modulated radiation treatments of head
and neck cancers: a treatment design study. Int J Radiat Oncol
Biol Phys 2000, 46:195-205.
2. Cozzi L, Fogliata A: IMRT in the treatment of head and neck
cancer: is the present already the future? Expert Rev Anticancer
Ther 2002, 2:297-308.
3. Eisbruch A, Dawson LA, Kim HM, Bradford CR, Terrell JE, Chepeha
DB, Teknos TN, Anzai Y, Marsh LH, Martel MK, Ten Haken RK, Wolf
GT, Ship JA: Conformal and intensity modulated irradiation of
head and neck cancer: the potential for improved target
irradiation, salivary gland function, and quality of life. Acta
Otorhinolaryngol Belg 1999, 53:271-275.
4. Mackie TR, Scrimger JW, Battista JJ: A convolution method of cal-
culating dose for 15-MV x rays. Med Phys 1985, 12:188-196.
5. Yu CX, Mackie TR, Wong JW: Photon dose calculation incorpo-
rating explicit electron transport. Med Phys 1995,
22:1157-1165.
6. Arnfield MR, Siantar CH, Siebers J, Garmon P, Cox L, Mohan R: The
impact of electron transport on the accuracy of computed
dose. Med Phys 2000, 27:1266-1274.
7. Mohan R, Arnfield M, Tong S, Wu Q, Siebers J: The impact of fluc-

tuations in intensity patterns on the number of monitor
units and the quality and accuracy of intensity modulated
radiotherapy. Med Phys 2000, 27:1226-1237.
8. Keall PJ, Siebers JV, Jeraj R, Mohan R: The effect of dose calcula-
tion uncertainty on the evaluation of radiotherapy plans.
Med Phys 2000, 27:478-484.
9. Jeraj R, Keall PJ, Siebers JV: The effect of dose calculation accu-
racy on inverse treatment planning. Phys Med Biol 2002,
47:391-407.
10. Siebers JV, Keall PJ, Kim JO, Mohan R: A method for photon beam
Monte Carlo multileaf collimator particle transport. Phys
Med Biol 2002, 47:3225-3249.
11. Kim JO, Siebers JV, Keall PJ, Arnfield MR, Mohan R: A Monte Carlo
study of radiation transport through multileaf collimators.
Med Phys 2001, 28:2497-2506.
12. Keall PJ, Siebers JV, Arnfield M, Kim JO, Mohan R: Monte Carlo
dose calculations for dynamic IMRT treatments. Phys Med Biol
2001, 46:929-941.
13. Yang J, Li JS, Qin L, Xiong W, Ma CM: Modelling of electron con-
tamination in clinical photon beams for Monte Carlo dose
calculation. Phys Med Biol 2004, 49:2657-2673.
14. Yang J, Li J, Chen L, Price R, McNeeley S, Qin L, Wang L, Xiong W,
Ma CM: Dosimetric verification of IMRT treatment planning
using Monte Carlo simulations for prostate cancer. Phys Med
Biol 2005, 50:869-878.
15. Wang L, Yorke E, Chui CS: Monte Carlo evaluation of 6 MV
intensity modulated radiotherapy plans for head and neck
and lung treatments. Med Phys 2002, 29:2705-2717.
16. Sakthi N, Keall P, Mihaylov I, Wu Q, Wu Y, Williamson JF, Schmidt-
Ullrich R, Siebers JV: Monte Carlo-based dosimetry of head-

and-neck patients treated with SIB-IMRT. Int J Radiat Oncol Biol
Phys 2006, 64:968-977.
17. Rogers DWO, Faddegon BA, Ding GX, Ma CM, We J, Mackie TR:
BEAM: a Monte Carlo code to simulate radiotherapy treat-
ment units. Med Phys 1995, 22:503-524.
18. Ma CM, Pawlicki T, Jiang SB, Li JS, Deng J, Mok E, Kapur A, Xing L, Ma
L, Boyer AL: Monte Carlo verification of IMRT dose distribu-
tions from a commercial treatment planning optimization
system. Phys Med Biol 2000, 45:2483-2495.
19. Ma CM, Mok E, Kapur A, Pawlicki T, Findley D, Brain S, Forster K,
Boyer AL: Clinical implementation of a Monte Carlo treat-
ment planning system. Med Phys 1999, 26:2133-2143.
20. Ma CM, Li JS, Pawlicki T, Jiang SB, Deng J, Lee MC, Koumrian T, Lux-
ton M, Brain S: A Monte Carlo dose calculation tool for radio-
therapy treatment planning. Phys Med Biol 2002, 47:1671-1689.
21. Leal A, Sanchez-Doblado F, Arrans R, Rosello J, Pavon EC, Lagares JI:
Routine IMRT verification by means of an automated Monte
Carlo simulation system.
Int J Radiat Oncol Biol Phys 2003,
56:58-68.
22. Francescon P, Cora S, Chiovati P: Dose verification of an IMRT
treatment planning system with the BEAM EGS4-based
Monte Carlo code. Med Phys 2003, 30:144-157.
23. Fan J, Li J, Chen L, Stathakis S, Luo W, Du Plessis F, Xiong W, Yang J,
Ma CM: A practical Monte Carlo MU verification tool for
IMRT quality assurance. Phys Med Biol 2006, 51:2503-2515.
24. Boudreau C, Heath E, Seuntjens J, Ballivy O, Parker W: IMRT head
and neck treatment planning with a commercially available
Monte Carlo based planning system. Phys Med Biol 2005,
50:879-890.

25. Dogan N, Siebers JV, Keall PJ, Lerma F, Wu Y, Fatyga M, Williamson
JF, Schmidt-Ullrich RK: Improving IMRT dose accuracy via deliv-
erable Monte Carlo optimization for the treatment of head
and neck cancer patients. Med Phys 2006, 33:4033-4043.
26. Spirou SV, Chui CS: Generation of arbitrary intensity profiles
by combining the scanning beam with dynamic multileaf col-
limation. Med Phys 1996, 23:1-8.
27. Spirou SV, Chui CS: Generation of arbitrary intensity profiles
by dynamic jaws or multileaf collimators. Med Phys 1994,
21:1031-1041.
28. Mihaylov IB, Lerma FA, Wu Y, Siebers JV: Analytic IMRT dose cal-
culations utilizing Monte Carlo to predict MLC fluence mod-
ulation. Med Phys 2006, 33:828-839.
29. Wu Q, Mohan R: Algorithms and functionality of an intensity
modulated radiotherapy optimization system. Med Phys 2000,
27:701-711.
30. An ADAC Pinnacle3 Physics Guide. ADAC Laboratories, Milpi-
tas, California; 2001.
31. Nelson WR, Hirayama H, Rogers DWO: The EGS4 Code System.
SLAC-265, Stanford Linear Accelerator Center 1985.
32. Ma CM, Reckwerdt PJ, Holmes M, Roger DWO, Geiser B: DOSXYZ
user's manual. PIRS-0509b. NRCC, Ottawa, Canada 1995.
33. Miften M, Wiesmeyer M, Kapur A, Ma CM: Comparison of RTP
dose distributions in heterogeneous phantoms with the
BEAM Monte Carlo simulation system. J Appl Clin Med Phys
2001, 2:21-31.
34. Jeraj R, Keall P: The effect of statistical uncertainty on inverse
treatment planning based on Monte Carlo dose calculation.
Phys Med Biol 2000, 45:3601-3613.
35. Dogan N, Siebers JV, Keall PJ: Clinical comparison of head and

neck and prostate IMRT plans using absorbed dose to
medium and absorbed dose to water. Phys Med Biol 2006,
51:4967-4980.
36. Siebers JV, Keall PJ, Nahum AE, Mohan R: Converting absorbed
dose to medium to absorbed dose to water for Monte Carlo
based photon beam dose calculations. Phys Med Biol 2000,
45:983-995.
37. Siebers JV, Keall PJ, Libby B, Mohan R: Comparison of EGS4 and
MCNP4b Monte Carlo codes for generation of photon phase
Publish with BioMed Central and every
scientist can read your work free of charge
"BioMed Central will be the most significant development for
disseminating the results of biomedical research in our lifetime."
Sir Paul Nurse, Cancer Research UK
Your research papers will be:
available free of charge to the entire biomedical community
peer reviewed and published immediately upon acceptance
cited in PubMed and archived on PubMed Central
yours — you keep the copyright
Submit your manuscript here:
/>BioMedcentral
Radiation Oncology 2009, 4:18 />Page 17 of 17
(page number not for citation purposes)
space distributions for a Varian 2100C. Phys Med Biol 1999,
44:3009-3026.
38. Libby B, Siebers J, Mohan R: Validation of Monte Carlo gener-
ated phase-space descriptions of medical linear accelerators.
Med Phys 1999, 26:1476-1483.
39. Keall PJ, Siebers JV, Libby B, Mohan R: Determining the incident
electron fluence for Monte Carlo-based photon treatment

planning using a standard measured data set. Med Phys 2003,
30:574-582.
40. Low DA, Harms WB, Mutic S, Purdy JA: A technique for the quan-
titative evaluation of dose distributions. Med Phys 1998,
25:656-661.
41. Harms WB Sr, Low DA, Wong JW, Purdy JA: A software tool for
the quantitative evaluation of 3D dose calculation algo-
rithms. Med Phys 1998, 25:1830-1836.
42. Wendling M, Zijp LJ, McDermott LN, Smit EJ, Sonke JJ, Mijnheer BJ,
van Herk M: A fast algorithm for gamma evaluation in 3D.
Med Phys 2007, 34:1647-1654.
43. Mihaylov IB, Lerma FA, Fatyga M, Sibers JV: Quantification of the
impact of MLC modeling and tissue heterogeneities on
dynamic IMRT dose calculations. Med Phys 2007, 34:1244-1252.